3D Visualization of Depression and Anxiety
Bibliographic record
Abstract
We present a novel and technologically enhanced teaching tool geared towards both patients, students, and clinicians with the primary goal of educating these groups about the anatomical and biochemical implications of various syndromes involving depression and anxiety. Our secondary goal is to inform patients, students, and clinicians about the most common treatment methods used to treat these syndromes. We achieved this by mapping the mesolimbic system, otherwise known as the “reward pathway,” and other relevant neuroanatomical structures using a high‐resolution cryosection dataset made available by the Big Brain project at McGill University and constructing 3D visualized models using 3D analysis software tools. The results of this project were compiled into a series of three interactive educational modules serving each of our goals. The first module described the neuroanatomical structures involved in syndromes of depression and anxiety along with their functions. The second module describes various forms of clinical depression and treatment options, including the mechanism of action and how it applies to the underlying disease process. The third module did the same but described anxiety rather than depression. This tool is valuable to patients and care providers alike because it explains neuroanatomical and biochemical aspects of these syndromes in a straight‐forward and easy‐to‐understand visual manner. It also describes the context of the best pharmacological interventions for each syndrome, which is a substantial step to help engage patients in their personalized mental health treatment plan. Support or Funding Information Thank you to Dr. David Dufeau and Marian University College of Osteopathic Medicine for the resources and guidance throughout this research project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".